The fact that neural networks are black-box models raises serious concerns for applications where algorithmic decisions have life-changing consequences, for instance in societal applications or for high risk industrial systems. This issue has motivated a substantial research effort over the last few years to investigate both explainability, and the creation and propagation of bias in algorithmic decisions. An important part of this research effort has been made to explain the predictions of black-box ML models [1]}, [2]}, [3]}, [4]} or to detect out-of-distribution data [5]}, [6]}.
